Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics Karunarathne,MS, Jones,SA, Ekanayake,SW and Pathirana,PN 2014, Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics, in Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014, Institute of Electrical and Electronics Engineers Inc.,, pp. 137-142, doi: 10.1109/BDCloud.2014.62. Attached Files Name Description MIMEType Size Downloads Title Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics Author(s) Karunarathne,MS Jones,SA Ekanayake,SW Pathirana,PN orcid.org/0000-0001-8014-7798 Conference name BDCloud 2014 : Big Data and Cloud Computing Conference location Sydney, Australia Conference dates 3-5 Dec. 2014 Title of proceedings Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014 Publication date 2014 Start page 137 End page 142 Publisher Institute of Electrical and Electronics Engineers Inc. Keyword(s) Cloud Computing Human Activity Monitoring Inertial Sensors Mobile Applications Physiotherapy Sport Summary Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services. ISBN 9781479967193 Language eng DOI 10.1109/BDCloud.2014.62 Field of Research 080505 Web Technologies (excl Web Search) 080502 Mobile Technologies 090609 Signal Processing Socio Economic Objective 970109 Expanding Knowledge in Engineering HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2014, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30071943 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Engineering Institute for Frontier Materials GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 01 Apr 2015, 08:44:45 EST Wed, 01 Apr 2015, 08:46:05 EST Wed, 01 Apr 2015, 08:47:49 EST Thu, 02 Apr 2015, 04:00:10 EST Thu, 02 Apr 2015, 05:08:13 EST Thu, 07 May 2015, 16:25:26 EST Thu, 07 May 2015, 16:26:19 EST Mon, 25 May 2015, 13:31:46 EST Tue, 26 May 2015, 08:13:09 EST Wed, 11 Jan 2017, 09:45:25 EST Filtered Full Citation counts:   Cited 9 times in TR Web of Science Cited 12 times in Scopus Search Google Scholar Access Statistics: 305 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Wed, 01 Apr 2015, 08:47:49 EST

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